It depends on the system configuration that you have. I had a laptop which had 8 GB of RAM and a Core i5 processor due to which huge amount of data made my R kernel crash (Google Predictions data from Kaggle which was around 3.2 GB in size)

Now, I have an upgraded desktop with 16 GB of RAM and Core i5 8K series, added with a RTX 2070 video card and this system can take in huge sets of data like a charm.

If in your case, you cannot invest in high-end systems, I would suggest you to use Kaggle's online kernel (they are pretty high-end) to perform your required analysis. Because a low-end PC would not have the require resource to handle huge sets of data as a result of which your R Studio might crash or the work space won't load up.

Hope this helps.

You can follow this link :

https://www.analyticsvidhya.com/blog/2015/12/7-important-ways-summarise-data/

Thank you Kashyap Barua for providing system requirement for using large scale data and suggestion regarding Kaggle's online kernel . I will look into it.

Thank you Nazrul Islam and M. Raihan for sharing useful link. Really this will help me while using large scale survey data.

welcome Gulshan Kumar

You can follow this link to get some tips

https://rpubs.com/msundar/large_data_analysis

This document could be helpful as well!

Thank you Cyprien Kengne Nde for sharing very interesting and informative link.